🐍📦 pyppbox#
pyppbox is an all-in-one Python toolbox which can be used for detecting, tracking, and re-identifying people with only a few lines of code. It is originally made for PoseTReID framework which can effectively track specific/certain people across multiple cameras or video scenes in distributed contexts of people interaction spaces such as malls or amusement parks, etc.
pyppbox = Python + People + ToolboxDesign for both short and long term people detecting, tracking, and re-identifying.
Integrate GUI for easy configuration and demo.
Support dictionary and YAML/JSON configuration.
Support [GTA_V_Dataset] -> Real-time online and offline evaluation.
pyppbox 4.0.0#
Version 4.0.0 adds RF-DETR detection, ArcFace/AdaFace recognition, and laprust assignment solving. It improves pipeline cleanup, configuration validation, and result exports, while preserving existing V3 configuration files.
Install or upgrade to the general release, including from a release candidate:
python -m pip install --upgrade "pyppbox==4.0.0"
Core compatibility covers Python 3.9–3.13. RF-DETR, ArcFace, and AdaFace require Python 3.10+ and optional dependencies. Install the model/data wheels for your selected modules using the setup guide. See the V4 upgrade guide for dependency changes, UTF-8 text files, and lifecycle APIs, or the release notes for the full changes.
Table of contents#
- 🚀 Getting Started
- 💡 Examples
- Example 1: Use some basic standalone functions
- Example 2: Use pyppbox’s internal config directory
- Example 3: Set a custom config directory
- Example 4: Set the main detector/tracker/reider by a YAML or JSON file
- Example 5: Set the main modules by a raw/ready dictionary
- Example 6: Set the main modules by names
- Example 7: Use result tools
- Example 8: Use real-time evaluation tools
- Example 9: Compare a generated result to a reference or ground-truth
- Example 10: Use GT (Ground-truth) as a detector
- Example 11: Train a classifier using a supported reider
- Example 12: Retrain GTA V classifiers for all supported reiders
- Example 13: Use a supported module directly
- Example 14: Use pyppbox in multithreading application
- Example 15: Use RF-DETR detection and SORT tracking
- Example 16: Export an AdaFace embedding model to ONNX
- Example 17: Compare FaceNet, ArcFace, and AdaFace
- Example 18: Recognize faces and train an ArcFace or AdaFace classifier
- V4 upgrade guide
- Standalone Module
- Supported Modules
- Structure
- Configurations
- Utilities
- Release Notes
